What are the necessary conditions of the phenomenon (output / outcome condition) you are studying in your research? Necessary Condition Analysis (NCA) helps exploring cause-effect relations in terms of "necessary but not sufficient". In the absence of the right level of the condition a certain effect cannot occur. This is independent of other causes, thus the necessary condition can be a bottleneck, critical factor, constraint, disqualifier and so on. In practice, in the absence of the right level of the necessary condition failure is guaranteed. Other causes cannot compensate for necessary conditions.
NCA is potentially applicable to the study of social networks, and can provide insightful results even when other analyses such as regression analysis show no or weak effects. By introducing a different logic and data analysis approach, NCA can contribute both to the rigor and relevance to theory, data analysis, and publications. It can be used in both quantitative as well as qualitative research. In this seminar we introduce NCA as underlying logic, a data analysis approach as well as explore potential synergies between NCA and Social Network Analysis.